Lexicon-Based Sentiment Analysis of Twitter Messages in Spanish

Antonio Moreno-Ortiz , Chantal Pérez Hernández


Lexicon-Based approaches to Sentiment Analysis (SA) differ from the more common machine-learning based approaches in that the former rely solely on previously generated lexical resources that store polarity information for lexical items, which are then identified in the texts, assigned a polarity tag, and finally weighed, to come up with an overall score for the text. Such SA systems have been proved to perform on par with supervised, statistical systems, with the added benefit of not requiring a training set. However, it remains to be seen whether such lexically-motivated systems can cope equally well with extremely short texts, as generated on social networking sites, such as Twitter. In this paper we perform such an evaluation using
Sentitext, a lexicon-based SA tool for Spanish.

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